def fit(frame_nums, model='q2'):
t, c = get_offtime_cdf(frame_nums)
if (model == 'q2'):
res = fh.FitModel(qpaint2mod, np.sqrt([0.5, 3.5, 500.]), c, t)
else:
res = fh.FitModel(qpaint1mod, np.sqrt([500., ]), c, t)
return res[0]**2
def OnGaussianFit(self, event):
import PYME.Analysis._fithelpers as fh
def gmod(p,x):
A, x0, sig, b = p
return A*pylab.exp(-(x-x0)**2/(2*sig**2)) + b
pylab.figure()
cols = ['b','g','r']
xv = self.image.xvals
for chan in range(self.image.data.shape[3]):
I = self.image.data[:,0,0, chan].squeeze().astype('f')
res = fh.FitModel(gmod, [I.max()-I.min(), xv[I.argmax()], xv[1] - xv[0], I.min()], I, xv)
pylab.plot(xv, I, cols[chan] + 'x', label=self.image.names[chan])
pylab.plot(xv, gmod(res[0], self.image.xvals), cols[chan], label='%2.3g, %2.3g, \n%2.3g, %2.3g' % tuple(res[0]))
print((res[0]))
#imo = self.image.parent
pylab.legend()
#rawIntensity.processIntensityTrace(I, imo.mdh, dt=imo.mdh['Camera.CycleTime'])
pylab.show()
def plot_and_fit(frame_nums, model='q2'):
import matplotlib.pyplot as plt
plt.figure(figsize=(6, 3))
plt.subplot(121)
t, c = get_offtime_cdf(frame_nums)
plt.plot(t, c, 'x')
t_ = np.logspace(0, np.log10(t.max()))
if (model == 'q2'):
res = fh.FitModel(qpaint2mod, np.sqrt([0.5, 3.5, 500.]), c, t)
f = qpaint2mod(res[0], t_)
else:
res = fh.FitModel(qpaint1mod, np.sqrt([500.,]), c, t)
f = qpaint1mod(res[0], t_)
plt.plot(t_, f)
plt.grid()
print(res[0]**2)
#plt.semilogy()
#plt.figure()
plt.subplot(122)
plt.plot(t, 1-c, 'x')
plt.plot(t_, 1- f)
plt.semilogy()
plt.ylim((1-c).min(), 1)
plt.grid()
return res[0]**2
#plt.plot()
def OnCalcMSDs(self, event=None):
#TODO - move this logic to reports - like dh5view module
# import pylab
import matplotlib.pyplot as plt
from PYME.Analysis import _fithelpers as fh
from PYME.Analysis.points.DistHist import msdHistogram
def powerMod(p,t):
D, alpha = p
return 4*D*t**alpha #factor 4 for 2D (6 for 3D)
pipeline = self.visFr.pipeline
clumps = set(pipeline['clumpIndex'])
dt = pipeline.mdh.getEntry('Camera.CycleTime')
Ds = np.zeros(len(clumps))
Ds_ = np.zeros(pipeline['x'].shape)
alphas = np.zeros(len(clumps))
alphas_ = np.zeros(pipeline['x'].shape)
error_Ds = np.zeros(len(clumps))
plt.figure()
for i, ci in enumerate(clumps):
I = pipeline['clumpIndex'] == ci
x = pipeline['x'][I]
y = pipeline['y'][I]
t = pipeline['t'][I]
nT = int((t.max() - t.min())/2)
h = msdHistogram(x, y, t, nT)
t_ = dt*np.arange(len(h))
plt.plot(t_, h)
res = fh.FitModel(powerMod, [h[-1]/t_[-1], 1.], h, t_)
Ds[i] = res[0][0]
Ds_[I] = res[0][0]
alphas[i] = res[0][1]
alphas_[I] = res[0][1]
print((res[0]))#, res[1]
if not res[1] is None:
error_Ds[i] = np.sqrt(res[1][0,0])
else:
error_Ds[i] = -1e3
plt.figure()
plt.scatter(Ds, alphas)
pipeline.addColumn('diffusionConst', Ds_, -1)
pipeline.addColumn('diffusionExp', alphas_)
pipeline.Rebuild()
